Method and system for biometric recognition

ABSTRACT

High quality, high contrast images of an iris and the face of a person are acquired in rapid succession in either sequence by a single sensor and one or more illuminators, preferably within less than one second of each other, by changing the data acquisition settings or illumination settings between each acquisition.

RELATED APPLICATIONS

This application is a continuation of, and claims priority to U.S.patent application Ser. No. 15/589,752, filed on May 8, 2017, entitled“Method and System for Biometric Recognition”, which is a continuationof, and claims priority to U.S. patent application Ser. No. 14/657,415,filed on Mar. 13, 2015, entitled “Method and System for BiometricRecognition”, issued as U.S. Pat. No. 9,646,217 on May 9, 2017, which isa continuation of, and claims priority to U.S. patent application Ser.No. 13/787,369, filed on Mar. 6, 2013, entitled “Method and System forBiometric Recognition”, which is a continuation of, and claims benefitto U.S. patent application Ser. No. 12/596,019, filed on Oct. 15, 2009,entitled “Method and System for Biometric Recognition” issued as U.S.Pat. No. 8,953,849 on Feb. 10, 2015, which claims priority toInternational application PCT/US2008/60791, filed Apr. 18, 2008,entitled “Method and System for Biometric System”, which claims priorityto U.S. provisional application No. 60/925,259, filed Apr. 19, 2007,titled “Method of Improving Performance of Face Recognition Systems”,all of which are hereby incorporated by reference in their entiretiesfor all references.

BACKGROUND

This disclosure relates generally to systems and methods wherein imageryis acquired primarily to determine or verify the identity of anindividual person using biometric recognition.

Biometric recognition methods are widespread and are of great interestin the fields of security, protection, financial transactionverification, airports, office buildings, but prior to the inventiontheir ability to correctly identify individuals, even when searchingthrough a small reference database of faces or irises, has always beenlimited, most notably when more than one biometric type is acquired froma single sensor or more generally when poor quality biometric data isacquired. In such cases there are typically false positives (which meansthat the incorrect person was identified) or false negatives (meaningthat the correct person was not identified).

There are several reasons for such poor performance of biometricrecognition methods.

First, when comparing a probe face to a reference face, it is importantthat the biometric templates or features are registered so thatcorresponding features (nose position for example) can be comparedaccurately. Even small errors in registration can result in matchingerrors even if the faces being compared are from the same person.

Second, for facial or iris recognition, it is important that therecognized face or iris and reference face or iris have the same, orvery similar, pose. Pose in this context means orientation (pan, tilt,yaw) and zoom with respect to the camera. Variations in pose between theimages again results in matching errors even if the faces being comparedare from the same person.

Third, the dynamic range or sensitivity of the camera sensor, opticalsystem and digitization system (the Data Acquisition System) may not besufficient to capture biometric information. For example, some biometricsystems are multi-modal, which means that they use several biometrics(for example, iris and face) either to improve the accuracy ofrecognition or to provide a quality image of the face for humaninspection. In such multiple biometric systems and methods there areproblems in assuring that each of the sets of data are from the sameperson, for example the system may unintentionally capture the face of afirst individual and iris of a second individual, resulting in anidentification or match failure or incorrect association of the facefrom one person with the iris of another person, for example. Anotherproblem with such multiple biometric systems is difficulty of obtaininggood data for each of the separate biometrics, e.g., face and irisbecause, for example, the albedo or reflectance of one biometricmaterial (the iris for example) may be very different to the albedo of asecond biometric (the face for example). The result is that the signaldetected of one of the two biometrics is outside the dynamic range orsensitivity of the Data Acquisition System and are either saturated orin the dark current region of the Acquisition System's sensitivity orsimply appears as a uniform gray scale with very poor contrast ofbiometric features, while the second biometric signal is within thedynamic range or sensitivity of the Data Acquisition System and hassufficient signal to noise ratio to enable accurate biometric or manualrecognition.

Fourth, the illumination may vary between the images being matched inthe biometric recognition system. Changes in illumination can result inpoor match results since detected differences are due to theillumination changes and not to the fact that a different person isbeing matched. In addition, due to the variability in illumination anddue to the limited dynamic range or sensitivity of the Data AcquisitionSystem, only some features of the entire biometric (fingerprint or facefor example) may be within range of the Data Acquisition System andtherefore suitable for biometric matching. This can reduce the number offeatures available for matching and also greatly reduces biometricaccuracy.

Since reflectance of a face is different from that of an iris, acquiringan image of an iris and a face from the same person with a single sensoraccording to prior methods and systems has yielded poor results. Pastpractice required two cameras or sensors or, in the cases of one sensor,the sensor and illuminators were operated at constant settings.

For example, Adam, et al., US Pat. Publ. 20060050933 aims to address theproblem of acquiring data for use in face and iris recognition using onesensor, but does not address the problem of optimizing the imageacquisition such that that the data acquired is optimal for each of theface and iris recognition components separately.

Determan, et al., U.S. Pat. Publ. 20080075334 and Saitoh, et al., U.S.Pat. Publ. 20050270386 disclose acquiring face and iris imagery forrecognition using a separate sensor for the face and a separate sensorfor the iris. Saitoh claims a method for performing iris recognitionthat includes identifying the position of the iris using a face and irisimage, but uses two separate sensors that focus separately on the faceand iris respectively and acquires data simultaneously such that usermotion is not a concern.

Determan also discusses using one sensor for both the face and iris, butdoes not address the problem of optimizing the image acquisition suchthat that the data acquired is optimal for each of the face and irisrecognition components separately.

Jacobson, et al., in US Pat. Publ. 20070206840 also describes a systemthat includes acquiring imagery of the face and iris, but does notaddress the problem of optimizing the image acquisition such that thatthe data acquired is optimal for each of the face and iris recognitioncomponents separately.

SUMMARY

We have discovered a method and related system for carrying out themethod which captures a high quality image of an iris and the face of aperson with single sensor or camera having a sensor by acquiring atleast two images with small time elapse between each acquisition bychanging the sensor or camera settings and/or illumination settingsbetween the iris acquisition(s) and the face acquisition(s).

The system comprises a sensor, illuminator, and processor adapted toacquire a high quality image of an iris of the person at a first set ofparameters. The parameters which can be varied between the firstbiometric recognition step and the second biometric recognition step,for example between iris and face recognition steps, can include one ormore of the following parameters of the Data Acquisition System, bymeans of example: illumination power setting, camera integration time,wavelengths, frame grabber gain and offset, and frame grabberlook-up-table. The acquisitions of the first biometric and the secondbiometric are within one second of each other, preferably within lessthan one second of each other. For example, the elapsed time betweenrecognition steps where the parameters are varied can be as little as0.5, 0.25, 0.1, 0.05, or even less, depending on the capability of thesensor, illuminators, and processor.

The settings on the illuminator and/or sensor are also changed withinone second, and within one half, one quarter, or even faster than onetenth of a second, depending on the embodiment.

Some embodiments include the steps of, and related system components ormodules for, identifying one or more acquired images containing the irisor face, performing registration over a captured sequence between theidentified acquired image, constraining the search for the iris or facein the remainder of the sequence in response to the results of theoriginal identified image, and the recovered registration parametersacross the sequence.

Certain embodiments of the invention include determining a distance fromthe sensor by comparing a diameter of the iris in the iris image with areference table and/or comparing an separation value between two eyes ofthe person with a reference table.

The system and method in some cases can adjust focus or zoom as afunction of a measured distance between two eyes of the person and/oradjust illumination based on the distance from the sensor, the distancecalculated by comparing a diameter of the iris in an iris image with areference table.

In certain cases the method comprises changing one or more sensorsettings of the Data Acquisition System between the acquisition of theface and iris selected from the group consisting of sensor integrationtime, illumination, shutter speed, aperture, gain and offset of thecamera, gain and offset of the frame grabber, and look-up tables in theframe grabber that may select bits of precision that are different fromthat available from the camera sensor output. The parameters which canbe varied between the first image and the second image can be, forexample, illumination pulse setting, illumination amplitude setting,camera integration time, camera gain setting, camera offset setting,camera wavelength, and frame grabber settings such as the look-up table.

It is sometimes beneficial for the system to compute the diameter,eccentricity and orientation of the iris upon acquisition of the imageof the iris with the sensor, to estimate eye separation, pose of theiris, and/or pose the face.

BRIEF DESCRIPTION OF THE DRAWINGS

The features and advantages of the invention will be appreciated byreference to the detailed description when considered in connection withthe attached drawings wherein:

FIG. 1 is a schematic of a face of a person wherein the face featuresare captured within the dynamic range of the sensor or image grabbingdevice (the Data Acquisition System) with sufficient contrast foraccurate facial recognition, while on the other hand the iris featuresare captured either outside the dynamic range of the sensor or imagegrabbing device, or without sufficient contrast for accurate irisrecognition.

FIG. 2 is a schematic of the face of the person of FIG. 1 wherein theiris features are captured within the dynamic range of the sensor orimage grabbing device and with sufficient contrast for accurate irisrecognition, while on the other hand the face features are capturedeither outside the dynamic range of the sensor or image grabbing device,or without sufficient contrast for accurate facial recognition

FIG. 3 is a plan view of an image acquisition system comprising a singlesensor in a camera and a set of two illuminators.

DETAILED DESCRIPTION

In the following detailed description, certain embodiments will beillustrated by reference to the drawings, although it will be apparentthat many other embodiments are possible according to the invention.

Referring first to FIG. 1, a face 10 is illustrated wherein facefeatures, including corners of eyes 11, wrinkles 12, 13, 16, and 17, andcorners of mouth 14 and nose 18 are visible but iris 15 is of lowcontrast and in poor detail. Such an image could be obtained with lowillumination settings with the sensor.

FIG. 2 illustrates the same face 10 as in FIG. 1 but with the iris 15 ofhigh contrast and the face features seen in FIG. 1 not acquired by thesensor but rather the facial features are of low contrast 19.

FIG. 3 is a side view of an embodiment of a system according to theinvention wherein a subject 10 is to the left of the figure. Images ofthe subject are captured by means of the camera 31 and illumination isprovided by means of an illuminator or, in the illustrated embodiment,two sets of illuminators wherein first illuminator set 32 are infra-redwavelength illuminators, for example an Axis ACC IR Illuminator model20812, and second illuminator 33 is a set of Light Emitting Diode (LED)illuminators which have a broad wavelength spectrum, for example SiliconImaging white LED illuminator 2-61617. In the illustrated embodiment a 2megapixel resolution CCD camera 31 such as the Aegis PL-B956F model isused with the two sets of illuminators 32, 33. The illumination andcamera settings are controlled by the camera and illumination settingcontroller. The specific parameters controlled by the controller includebut are not limited to: camera gain, camera offset, camera integrationtime, camera look-up table selection, illumination pulse width forilluminator 1, illumination pulse width for illuminator 2, illuminationamplitude for illuminator 1, and illumination amplitude for illuminator2.

An optional range measurement module 30 can also be included. Thismodule 30 measures the approximate distance between the cameras and/orillumination and the subject 10. There are many devices known formeasuring range. These include stereo depth recovery methods, such asthat described by Horn in “Robot Vision”, MIT Press, pages 202-242, oran acoustic/ultrasonic range sensor such as that supplied by CampbellScientific Inc, model number SR50.

A second method to determine range is to measure the eye separation oriris diameter.

In one embodiment, the system comprises a sensor, lens system,illuminator, and processor adapted to acquire a high quality image of aniris of the person at a first set of illumination power setting, cameraintegration time, wavelengths and lens settings; and to acquire with thesame first sensor a high quality image of the face of the person at asecond set of illumination power setting, camera integration time,wavelengths and lens settings, wherein acquisitions of the face imageand iris image are within one second of each other, preferably withinless than one second of each other.

The settings on the illuminator and/or sensor and/or lens system arealso changed within one second, and within one half, one quarter, oreven faster than one tenth of a second, depending on the embodiment.

Some embodiments include the steps of, and related system components ormodules for, identifying one or more acquired images containing thebiometric data, for example the iris or the face, performingregistration over a captured sequence between the identified acquiredimage, constraining the search for the biometric data, including theiris or face, in the remainder of the sequence in response to theresults of the original identified image, and the recovered registrationparameters across the sequence. The recovered motion between the imagesmay be due to the motion of the person in the scene as they approach orrecede from the camera, or may be from motion induced by changes in thelens parameters, such as zooming of the lens or pan and tilt control ofthe camera.

Certain embodiments of the invention include determining a distance fromthe sensor by comparing a diameter of the iris in the iris image with areference table and/or comparing an separation value between two eyes ofthe person with a reference table.

The system and method in some cases can adjust focus or zoom as afunction of a measured distance between two eyes of the person and/oradjust illumination based on the distance from the sensor, the distancecalculated by comparing a diameter of the iris in an iris image with areference table.

In certain cases the method comprises changing one or more sensor andlens settings selected from the group consisting of integration time,illumination, shutter speed, aperture, and gain between the acquisitionsof the face and the iris.

It is sometimes beneficial for the system to compute the diameter,eccentricity and orientation of the iris upon acquisition of the imageof the iris with the sensor, to estimate eye separation, pose of theiris, and/or pose the face. In one embodiment, the wavelength andbrightness of the illumination are varied. More specifically, the cameraand illumination parameters are controlled as follows: The visibleilluminator 33 is set to provide constant illumination for all acquiredframes with a magnitude of 50 milliamps. The remaining IR illuminator 32is set to a constant pulse width of 6 msecs, but to a pulse magnitudethat varies between the two values of 0 milliamps and 400 milliampsbetween alternate frames that are acquired by the camera. The camera maybe set to acquire frames at 3 frames a second The camera integrationtime may be set at 6 msecs, and the camera gain and offset and cameralookup table may be set to constant values. Those constant values arechosen such that the image of the iris captured when the current isbeing passed to the Infra-Red illuminator has enough signal to noise foraccurate biometric matching. By this embodiment, the images acquiredwhen the current is not being passed to the infrared illuminator aresuitable for accurate facial recognition.

In a second embodiment, the camera integration time is varied. Morespecifically, the camera and illumination parameters are controlled asfollows: The visible illuminator is set to provide no illumination forany frame, or a constant illumination for all acquired frames with amagnitude of 50 milliamps. The remaining IR illuminator is set to aconstant pulse width of 6 msecs, and to a constant pulse magnitude of400 milliamps. The camera may be set to acquire frames at 3 frames asecond. The camera integration time is set to alternate between adjacentframes between the two values of 1.5 msecs and 6 msecs, and the cameragain and offset and frame grabber lookup table (and other parameters ofthe Data Acquisition System) may be set to constant values. Thoseconstant values are chosen such that the image of the iris captured whenthe camera uses the longer integration time has enough signal to noisefor accurate biometric matching. In this embodiment, the images acquiredwhen the shorter integration time are suitable for accurate facialrecognition.

A third embodiment is the same as the first embodiment, excepting thatthe magnitude of one or both of the visible illuminators (set at 50milliamps in embodiment 1) and IR illuminators (set at 400 milliamps inembodiment 1) is adjusted in response to an output of either theprocessor and/or the depth measurement sensor. More specifically, theprocessor or range measurement sensor provides an estimate of the rangeof the subject. This estimate is then used to look-up a preferredintensity magnitude for each of the visible and infra-red illuminatorswhich is then provided to the illuminators. These preferred values areselected by acquiring data from a wide range of subjects under differentintensity magnitude settings and at different distances, and byempirically finding the settings that provide the best performance forbiometric recognition of the face and iris respectively.

A fourth embodiment first acquires data as described in the firstembodiment. In a second step however the images that are optimized foracquiring data of each of the face and iris are aligned using themethods described in this invention, in order to remove any subject orcamera motion that may have occurred between the two time instants thateach of the optimized data was acquired from the sensor. In this way thefeatures that are optimal for facial recognition in one image can becorresponded to features that are optimal for iris recognition in theother image. This allows processing performed on one image to be used toconstrain the results of processing on the other image. For example,recovery of the approximate position and orientation of the face in oneimage can then be used to constrain the possible position andorientation of the iris in the second image. Similarly, recovery of theposition of the iris in one image constrains the possible position ofthe face in the second image. This can assist in reducing the processingtime for one or other of the biometric match processes, for example. Inanother example, some facial features are most accurately localized andhave best signal to noise properties under one set of camera orillumination conditions, whereas another set of facial features are mostaccurately localized and have best signal to noise properties underanother set of camera or illumination settings. This method allows themost accurately localized features of all facial features to be used forfacial recognition, thereby providing improved recognition performance.More specifically, the features can be combined by selecting whichfeatures from which image have highest signal to noise. There areseveral methods for feature selection, for example, a contrast measuresuch as an edge detector can be performed over the image (for examplesee Sobel, I., Feldman, G., “A 3×3 Isotropic Gradient Operator for ImageProcessing”, Pattern Classification and Scene Analysis, Duda, R. andHart, P., John Wiley and Sons, '73, pp 271-272), and the magnitude ofthe result can be used to select the feature from either image with thelargest contrast.

The image resolution typically required for face recognition isrecognized to be approximately 320×240 pixels, as documented in an ISOstandard. As part of the image acquisition system, however, we use acamera capable of imaging the face with a much higher resolution, forexample 1024×1024 pixels. This higher resolution data enables thedetection and localization of features that cannot be detected reliablyin the lower resolution data, and also enables more precise and robustdetection of features that could be seen in the lower resolutionimagery. For example, the precise location of the pupil boundary can berecovered in the high resolution imagery and typically cannot berecovered accurately in the lower resolution imagery. One method fordetecting the pupil/iris boundary is to perform a Hough transform, forexample. U.S. Pat. No. 3,069,654. The face recognition algorithm may usethe same high resolution data that is being captured or by using anadditional low resolution camera. An additional method for performingregistration is to perform alignment algorithms over the eye region. Inthis case the eye region in one face image is aligned to sub-pixelprecision to the eye region in another face image. Registration can alsobe done over the entire image or over the face region. Registration canbe performed for example by methods described in Horn, “Robot Vision”,MIT Press p 278-299. The precise localization information can be passedto the face recognition algorithm in order to improve its performance.

In addition to eye location, the image acquisition system also recoversthe zoom or distance of the person. This is accomplished by setting thehigh resolution camera to have a very narrow depth of field. This meansthat features of the face only appear sharply in focus at a specificdistance from the camera. Methods can be performed to detect when thosefeatures are sharply in focus, and then only those images are selectedfor face recognition. If a second lower resolution camera is used foracquiring the data used for face recognition, then processing performedon the high-resolution imagery to detect sharply-focused features isused to trigger image acquisition on the lower resolution camera. Thisensures that the face images used for face recognition are all at theidentical scale. There are several methods available to detectsharply-focused features. For example, an edge filter can be performedover the image (see Sobel, I., Feldman, G., “A 3×3 Isotropic GradientOperator for Image Processing”, Pattern Classification and SceneAnalysis, Duda, R. and Hart, P., John Wiley and Sons, '73, pp 271-272)and then squared at each pixel, and then averaged over the image inorder to compute an edge energy score. When the score is maximal orexceeds a threshold, then the person is within the depth of field of thehigh resolution camera.

Knowledge of the eye location as well as the zoom of the face allowsspecific sub-regions of the face to be selected and used for facerecognition. For example, one or more rectangular regions of a certainsize (in pixels) can be cut out from the high-resolution imagery andused as an input to a face recognition engine, even though only part ofthe face is being presented. The locations of certain areas, such as thenose, can be predicted using a model of a standard face, and usingknowledge of the eye location and the zoom. The face recognition engineis informed that only one or more specific subsets of the face are beingpresented. In this case we only provide a face recognition database tothe face recognition engine that comprises only the same specific subsetregions.

If a second camera is used to acquire data for face recognition (or anyother biometric recognition, such as ear recognition) then because thelocation of the first camera is different to that of the second camera,then recovering a precise pixel location in the first camera does notsimply translate into a corresponding pixel location in the secondcamera. We accomplish this using knowledge of the location of the depthof field of the first camera, which in turn provides a very precisedepth of the face with respect to the first camera. Given a pixellocation in the first camera, and the depth of the person, as well ascamera intrinsics (such as focal length, and relative cameratranslation) that can be calibrated in advance (see for example, “AnEfficient and Accurate Camera Calibration Technique for 3D MachineVision”, Roger Y. Tsai, Proceedings of IEEE Conference on ComputerVision and Pattern Recognition, Miami Beach, F L, 1986, pages 364-374),then it is known how to compute the precise pixel location of thecorresponding feature in the second camera (see Horn, “Robot Vision”,MIT Press, p 202-242 for example).

In addition to ensuring consistent zoom, we also take steps to ensureconsistent pose by detecting features in the high-resolution image thatwould not otherwise be visible with precision in the low resolutionimagery. For example, the pupil boundary is only near-circular if theperson is looking in the direction of the camera. A method for detectingcircular or non-circular boundaries is the Hough Transform, for examplesee U.S. Pat. No. 3,069,654. If imagery of the iris is near circular inthe narrow field of view imagery, then imagery of the face is in thelower resolution camera is more likely to be of a frontal view and ispassed to the facial recognition module. Similarly, the pose of the facecan be recovered and used to constrain the expected pose of the iris forsubsequent processing.

The image acquisition system also has a dynamic range control module.This module addresses the problem where data from two differentbiometrics (e.g. iris and face) cannot be reliably acquired because thedynamic range of the sensor, and the Data Acquisition System in general,is limited. We address this by two methods.

First, we acquire data at two different but controlled times in such away that at the first time instance we expect that the first biometricimagery (e.g., face) imagery will be within the dynamic range of thesensor given the specific illumination configuration. We then acquiredata at a second time instance where we expect the second biometricimagery (e.g. iris imagery) to be within the dynamic range orsensitivity of the sensor. For example, consider a configuration where acamera and an illuminator lie close to each other, and a person isapproaching the configuration. Images are continuously captured. As theperson approaches the configuration, then the reflectance off thebiometric tissue (face or iris) increases since the distance from theperson to the camera and illumination configuration is decreasing. Atone distance it can be expected that data corresponding to one biometricwill be within the dynamic range (e.g. face) while at a differentdistance, it can be expected that data corresponding to a secondbiometric can be within the dynamic range (e.g. iris). The camera mayhave a small depth of field due to the resolution requirements ofobtaining one biometric (e.g. the iris). However, the resolutionrequired for the other biometric may be much coarser so that blurringdue to imagery lying outside the depth of field has negligible impact onthe quality of data acquired for the other biometric (e.g. the face).

A specific implementation of this approach is to a) Acquire all imagesinto a stored buffer, b) detect the presence of an eye in the depth offield of the camera using the methods described earlier, c) compute thenumber of frames back in time where the person was situated at a furtherdistance from the depth of field region (and therefore illuminatedless), based on a prediction of their expected motion (which can be, forexample, a fixed number based on walking speed), and d) select thatimagery from the buffer to be used for face recognition. The eye andface location can be registered over time in the buffer to maintainknowledge of the precise position of the eyes and face throughout thesequence. Registration can be performed for example by Horn, “RobotVision”, MIT Press p 278-299

The second method for ensuring that data lies within the dynamic rangeof the camera is to modulate the magnitude of the illumination over atemporal sequence. For example, in one frame the illumination can becontrolled to be much brighter than in a subsequent frame. In oneimplementation, images are always acquired at a low illumination levelsuitable for one biometric. Features are detected that would only beobserved when the face is fully in focus and within the depth of fieldregion. For example, the Sobel image focus measure previously cited canbe used. When the face is near or within the depth of field region,based for example on a threshold of the focus measure, then theillumination can be increased in order to obtain imagery of the secondbiometric (e.g. the iris) within the dynamic range of the camera. Whenthe face has left the depth of field region, then the illumination canrevert back to the lower magnitude level.

In addition to modulating the magnitude of the illumination, we alsomodulate the wavelength of the illumination. This allows multipledatasets corresponding to the same biometric to be acquired and matchedindependently, but using the constraint that the data belongs to thesame person. For example, person A may match dataset Band C using datacaptured at one wavelength, but person A may match dataset C and D usingdata captured at a second wavelength. This gives evidence that person Amatches to dataset C. This approach is extended to not only includefusing the results of face recognition after processing, but also byincluding the multi-spectral data as a high dimensional feature vectoras an input to the face recognition engine.

In some embodiments there is an advantage to aligning the acquiredimages of the face and iris with the processor, thereby reducing theeffect of camera or subject motion that may have occurred between thetwo time instants that each of the images was acquired from the sensor.In this way the features that are optimal for facial recognition in oneimage can be corresponded to features that are optimal for irisrecognition in the other image. This allows processing performed on oneimage to be used to constrain the results of processing on the otherimage. For example, recovery of the approximate position and orientationof the face in one image can then be used to constrain the possibleposition and orientation of the iris in the second image. Similarly,recovery of the position of the iris in one image constrains thepossible position of the face in the second image. This can assist inreducing the processing time for one or other of the biometric matchprocesses, for example. In another example, some facial features aremost accurately localized and have best signal to noise properties underone set of camera or illumination conditions, whereas another set offacial features are most accurately localized and have best signal tonoise properties under another set of camera or illumination settings.This method allows the most accurately localized features of all facialfeatures to be used for facial recognition, thereby providing improvedrecognition performance.

The present invention, therefore, is well adapted to carry out theobjects and attain the ends and advantages mentioned, as well as othersinherent therein. While the invention has been depicted and describedand is defined by reference to particular preferred embodiments of theinvention, such references do not imply a limitation on the invention,and no such limitation is to be inferred. The invention is capable ofconsiderable modification, alteration and equivalents in form andfunction, as will occur to those ordinarily skilled in the pertinentarts. The depicted and described preferred embodiments of the inventionare exemplary only and are not exhaustive of the scope of the invention.Consequently, the invention is intended to be limited only by the spiritand scope of the appended claims, giving full cognizance to equivalentsin all respects.

I claim:
 1. A system for acquiring iris and face biometric features, thesystem comprising: a depth measurement sensor configured to determine adistance of an image sensor from a subject; at least one light source;and the image sensor, the image sensor configured to: acquire, at afirst time instant, a first image comprising a biometric feature of aface of the subject, the face illuminated by light of a first wavelengthfrom the at least one light source set to a first intensity magnitudesuitable for acquiring the first image at the determined distance; andacquire, at a second time instant, a second image comprising a biometricfeature of an iris of the subject, the iris illuminated by light of asecond wavelength from the at least one light source set to a secondintensity magnitude suitable for acquiring the second image at thedetermined distance, wherein the second time instant is within apredetermined time period of the first time instant sufficient to linkthe first image and the second image to the same subject.
 2. The systemof claim 1, wherein the depth measurement sensor is configured todetermine the distance of the image sensor from the subject bydetermining at least one of a diameter of the iris or a separationdistance between two eyes of the subject.
 3. The system of claim 1,further comprising at least one processor configured to performbiometric recognition using the first image comprising the biometricfeature of the face, and perform biometric recognition the second imagecomprising the biometric feature of the iris.
 4. The system of claim 1,further comprising at least one processor configured to determine anorientation of the face using the first image, and determine an expectedorientation and location of the iris on the second image using thedetermined orientation of the face.
 5. The system of claim 1, furthercomprising at least one processor configured to perform registrationbetween the first image comprising the biometric feature of the face andthe second image comprising the biometric feature of the iris.
 6. Thesystem of claim 1, wherein the at least one light source comprises avisible light illuminator configured to provide the light of the firstwavelength, and an infra-red light illuminator configured to provide thelight of the second wavelength.
 7. The system of claim 1, furthercomprising at least one processor configured to adjust at least one ofthe light of the first wavelength or the light of the second wavelengthresponsive to the determined distance.
 8. The system of claim 1, whereinthe depth measurement sensor is configured to determine the distance bycomparing at least one of a diameter of the iris or a separationdistance between two eyes of the subject, with one or more values of areference table.
 9. The system of claim 1, further comprising an imagingcontroller configured to adjust at least one of: sensor gain, sensoroffset, illumination pulse width, shutter speed, focus, zoom orintegration time for image acquisition, between the acquisition of thefirst image and the acquisition of the second image.
 10. The system ofclaim 1, wherein the image sensor is configured to acquire at least oneof the first image or the second image while the subject is in motion.11. A method for biometric acquisition, the method comprising:determining, by a depth measurement sensor, a distance of an imagesensor from a subject; acquiring, by the image sensor at a first timeinstant, a first image comprising a biometric feature of an iris of thesubject, the iris illuminated by light of a first wavelength set to afirst intensity magnitude suitable for acquiring the first image at thedetermined distance; and acquiring, by the image sensor at a second timeinstant, a second image of a biometric feature of a face of the subject,the face illuminated by light of a second wavelength set to a secondintensity magnitude suitable for acquiring the second image at thedetermined distance, wherein the second time instant is within apredetermined time period of the first time instant sufficient to linkthe first image and the second image to the same subject.
 12. The methodof claim 11, wherein determining the distance of the image sensor fromthe subject comprises determining at least one of a diameter of the irisor a separation distance between two eyes of the subject.
 13. The methodof claim 11, further comprising performing biometric recognition usingthe first image comprising the biometric feature of the iris, andperforming biometric recognition using the second image comprising thebiometric feature of the face.
 14. The method of claim 11, furthercomprising determining an orientation of the face using the secondimage, and determining an expected orientation and location of the irison the first image using the determined orientation of the face.
 15. Themethod of claim 11, further comprising performing registration betweenthe first image comprising the biometric feature of the iris and thesecond image of the biometric feature of the face.
 16. The method ofclaim 11, wherein the light of the second wavelength is provided by avisible light illuminator and the light of the first wavelength isprovided by an infra-red light illuminator.
 17. The method of claim 11,further comprising adjusting at least one of the light of the firstwavelength or the light of the second wavelength responsive to thedetermined distance.
 18. The method of claim 11, wherein determining thedistance comprises comparing at least one of a diameter of the iris or aseparation distance between two eyes of the subject, with one or morevalues of a reference table.
 19. The method of claim 11, furthercomprising adjusting, by an imaging controller, at least one of: sensorgain, sensor offset, illumination pulse width, shutter speed, focus,zoom or integration time for image acquisition, between the acquisitionof the first image and the acquisition of the second image.
 20. Themethod of claim 11, wherein the acquisition of at least one of the firstimage or the second image occurs while the subject is in motion.